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On the relevance of denoising and artefact reduction in 3D segmentation and classification within complex computed tomography imagery.

Mouton, A. and Breckon, T.P. (2019) 'On the relevance of denoising and artefact reduction in 3D segmentation and classification within complex computed tomography imagery.', Journal of X-ray science and technology., 27 (1). pp. 51-72.

Abstract

We evaluate the impact of denoising and Metal Artefact Reduction (MAR) on 3D object segmentation and classification in low-resolution, cluttered dual-energy Computed Tomography (CT). To this end, we present a novel 3D materials-based segmentation technique based on the Dual-Energy Index (DEI) to automatically generate subvolumes for classification. Subvolume classification is performed using an extension of Extremely Randomised Clustering (ERC) forest codebooks, constructed using dense feature-point sampling and multiscale Density Histogram (DH) descriptors. Within this experimental framework, we evaluate the impact on classification accuracy and computational expense of pre-processing by intensity thresholding, Non-Local Means (NLM) filtering, Linear Interpolation-based MAR (LIMar) and Distance-Driven MAR (DDMar) in the domain of 3D baggage security screening. We demonstrate that basic NLM filtering, although removing fewer artefacts, produces state-of-the-art classification results comparable to the more complex DDMar but at a significant reduction in computational cost - bringing into question the importance (in terms of automated CT analysis) of computationally expensive artefact reduction techniques. Overall, it was found that the use of MAR pre-processing approaches produced only a marginal improvement in classification performance (< 1%) at considerable additional computational cost (> 10×) when compared to NLM pre-processing.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.3233/XST-180411
Publisher statement:The final publication is available at IOS Press through https://doi.org/10.3233/XST-180411
Date accepted:15 September 2018
Date deposited:13 September 2018
Date of first online publication:01 April 2019
Date first made open access:15 September 2018

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